The categorization of visual input is one of the most essential challenges faced by our visual system. Despite its importance, however, the debate on the cortical origin and the timing of category-specific effects remains unsettled.

Here we investigate this issue by combining extensive category training of two artificial visual categories in nine subjects with an EEG and MEG adaptation paradigm. Importantly, we estimated category effects prior to category training, and again after 24 training sessions. This allowed us to investigate the exact timing of category effects while closely controlling for low-level stimulus properties, which have previously provided extensive challenges due to potential low-level confounds in naturally occurring visual categories.

High-level category-effects were assessed by means of visually evoked potentials (EEG) and fields (MEG) in response to the second stimulus of the adaptation paradigm, which could originate either from the same class as the first adapter stimulus or from the other class. Prior category training, no differences in the evoked potentials were observed, demonstrating a successful control for low-level stimulus properties. After training, however, we find significant, category-selective, differences in the rather early P100 (EEG, peak latency 108ms, p<0.05 two-tailed t-test) and M100 (MEG, peak latency 128ms, p<0.05 two-tailed t-test) components. Following this, we contrasted trials with correct and incorrect category-judgments and found significant category effects only for correct trials (p<0.05, two-tailed t-test) but not for incorrect ones (p>0.8, two-tailed t-test). This finding supports the behavioral relevance of the found category effects.

In conclusion, behaviorally relevant category effects emerge as a result of category training as early as 108ms after stimulus onset. The timing and topography of the effect renders the possibility of feedback from frontal areas unlikely and rather suggests the origin of category selective representations in the ventral stream.